Artificial Intelligence-Enabled Maternal and Fetal Monitoring Devices in Pregnancy: A Global Scoping Review and Evidence Mapping

Background: Artificial intelligence (AI) is increasingly used in obstetric ultrasound, cardiotocography, wearable fetal electrocardiography, and remote maternal-fetal monitoring. Evidence on real-world implementation, including feasibility, acceptability, workflow, task sharing, sustainability, equity, cost, and user experience, remains limited. This review aimed to map global real-world implementation evidence for AI-enabled obstetric monitoring and identify outcomes, determinants, gaps, and research priorities for scale-up. Methods: We conducted a scoping review using the Arksey and O’Malley framework with Levac refinements, Joanna Briggs Institute guidance, and PRISMA-ScR. PubMed/MEDLINE, Web of Science, and Dimensions were searched from January 2010 to May 2026 and Scopus from January 2010 to December 2025. We included studies on AI-enabled obstetric monitoring in clinical or community settings and primary research on implementation readiness. We classified findings as operational (Tier 1) or readiness (Tier 2), mapped outcomes to RE-AIM, and interpreted determinants using NASSS. Results: Thirteen studies met the criteria: ten on AI-enabled obstetric ultrasound, two on AI-assisted cardiotocography, and one on wearable fetal electrocardiography. Seven were Tier 1 and six Tier 2; ten were conducted in LMICs or mainly represented LMIC stakeholders. Included studies reported higher ultrasound process-quality measures, shorter expert-review time, feasible image acquisition, high acceptability, and potential for task sharing. Selection bias, confounding, short follow-up, and reliance on proximal outcomes limited inference. No randomized trial showed improved maternal or neonatal outcomes, and no complete cost-effectiveness evaluation met the criteria. Conclusions: Implementation evidence remains sparse, recent, and concentrated in AI-enabled ultrasound. Pragmatic comparative studies, long-term implementation research, economic evaluation, safety monitoring, referral completion, and equitable scale-up should be prioritized.